Breaking the field phenotyping bottleneck in maize with autonomous robots

Understanding phenotypic plasticity in maize (Zea mays L.) is a current grand challenge for continued crop improvement.  Measuring the interactive effects of genetics, environmental factors, and management practices such as nitrogen rate (GxExM) on crop performance is time-consuming, expensive, and a major bottleneck to continued yield advancement. We demonstrate that a novel autonomous robotic platform, capable of collecting biologically relevant and commonly measured phenotypes, within a maize canopy at high-throughput, low-cost, and high-volume is now a reality. Field teams used multiple TerraSentia autonomous ground robots developed by EarthSense, Inc. (Champaign, IL) to capture data using a suite of low-cost sensors from nearly 200,000 experimental units, located at 142 unique research fields in the USA and Canada, across five years. Novel computer vision and machine learning algorithms, developed by EarthSense, Inc., analyzed these in-canopy multi-sensor data to deliver groun..., The TerraSentia rover collected RGB video images and LIDAR point clouds from corn plants as it moved between rows in the field. Video data was processed by algorithms described in the paper to estimate either ear height, stem width, or leaf area index. Ground-truth phenotypes were collected from a subset of the same plots using established methods: plot combine to estimate grain yield, plant and ear height using a centimeter-scaled measuring pole, stem width with calipers, and leaf area index by manual measurement and a LICOR LI-3100C scanner. Both rover and manual measurements were from multiple plants per plot, and reported as means of individual measures per plot., , # Breaking the field phenotyping bottleneck in maize with autonomous robots

[https://doi.org/10.5061/dryad.m905qfvb6](https://doi.org/10.5061/dryad.m905qfvb6)

## Description of the data and file structure

These files contain data collected either by the TerraSentia robot or by manual measurements for validation.  Data is organized by the Figures presented in paper.  Each dataset includes metadata on year, location and date of collection, field coordinates (X-y grid) for each plot, the maize hybrid planted in each plot, and the phenotypic data values.

### Files and variables

#### File: Data\_for\_Figure\_S2\_Trait\_Validation.xlsx

**Description:** Data output from TerraSentia rover and ground-truth measures from the same plots, used to demonstrate accuracy of rover-estimated phenotypes. 

##### Variables

* Leaf Area Index (LAI), Stem Diameter, Plant Height, Ear Height, timestamp, location of plot in field column (x) and range (y) grid.

#### File: Data\_for\_Case\_studies\_1\_to\_...,